Chapter # 0: Introduction Dept of Economics: Kuwait University 405 ECONOMETRICS Chapter # 0: Introduction By: Domodar N. Gujarati Prof. M. El-Sakka Dept of Economics: Kuwait University

WHAT IS ECONOMETRICS? Econometrics means “economic measurement”. the scope of econometrics is much broader, as can be seen from the following definitions: “Consists of the application of mathematical statistics to economic data to lend empirical support to the models constructed by mathematical economics and to obtain numerical results” (Gerhard 1968). “Econometrics may be defined as the social science in which the tools of economic theory, mathematics, and statistical inference are applied to the analysis of economic phenomena” (Goldberger 1964). “Econometrics is concerned with the empirical determination of economic laws” (Theil 1971).

WHY ECONOMETRICS IS A SEPARATE DISCIPLINE? the subject deserves to be studied in its own right for the following reasons: Economic theory makes statements or hypotheses that are mostly qualitative in nature (the law of demand), the law does not provide any numerical measure of the relationship. This is the job of the econometrician. The main concern of mathematical economics is to express economic theory in mathematical form without regard to measurability or empirical verification of the theory. Econometrics is mainly interested in the empirical verification of economic theory. Economic statistics is mainly concerned with collecting, processing, and presenting economic data in the form of charts and tables. It does not go any further. The one who does that is the econometrician.

METHODOLOGY OF ECONOMETRICS Broadly speaking, traditional econometric methodology proceeds along the following lines: 1. Statement of theory or hypothesis. 2. Specification of the mathematical model of the theory 3. Specification of the statistical, or econometric, model 4. Collecting the data 5. Estimation of the parameters of the econometric model 6. Hypothesis testing 7. Forecasting or prediction 8. Using the model for control or policy purposes. To illustrate the preceding steps, let us consider the well-known Keynesian theory of consumption.

1. Statement of Theory or Hypothesis Keynes states that on average, consumers increase their consumption as their income increases, but not as much as the increase in their income (MPC < 1). 2. Specification of the Mathematical Model of Consumption (single-equation model) Y = β1 + β2X 0 < β2 < 1 (I.3.1) Y = consumption expenditure and (dependent variable) X = income, (independent, or explanatory variable) β1 = the intercept β2 = the slope coefficient The slope coefficient β2 measures the MPC.

Geometrically,

3. Specification of the Econometric Model of Consumption The relationships between economic variables are generally inexact. In addition to income, other variables affect consumption expenditure. For example, size of family, ages of the members in the family, family religion, etc., are likely to exert some influence on consumption. To allow for the inexact relationships between economic variables, (I.3.1) is modified as follows: Y = β1 + β2X + u (I.3.2) where u, known as the disturbance, or error, term, is a random (stochastic) variable that has well-defined probabilistic properties. The disturbance term u may well represent all those factors that affect consumption but are not taken into account explicitly.

(I. 3. 2) is an example of a linear regression model, i. e (I.3.2) is an example of a linear regression model, i.e., it hypothesizes that Y is linearly related to X, but that the relationship between the two is not exact; it is subject to individual variation. The econometric model of (I.3.2) can be depicted as shown in Figure I.2.

4. Obtaining Data To obtain the numerical values of β1 and β2, we need data. Look at Table I.1, which relate to the personal consumption expenditure (PCE) and the gross domestic product (GDP). The data are in “real” terms.

The data are plotted in Figure I.3

5. Estimation of the Econometric Model Regression analysis is the main tool used to obtain the estimates. Using this technique and the data given in Table I.1, we obtain the following estimates of β1 and β2, namely, −184.08 and 0.7064. Thus, the estimated consumption function is: Yˆ = −184.08 + 0.7064Xi (I.3.3) The estimated regression line is shown in Figure I.3. The regression line fits the data quite well. The slope coefficient (i.e., the MPC) was about 0.70, an increase in real income of 1 dollar led, on average, to an increase of about 70 cents in real consumption.

6. Hypothesis Testing That is to find out whether the estimates obtained in, Eq. (I.3.3) are in accord with the expectations of the theory that is being tested. Keynes expected the MPC to be positive but less than 1. In our example we found the MPC to be about 0.70. But before we accept this finding as confirmation of Keynesian consumption theory, we must enquire whether this estimate is sufficiently below unity. In other words, is 0.70 statistically less than 1? If it is, it may support Keynes’ theory. Such confirmation or refutation of economic theories on the basis of sample evidence is based on a branch of statistical theory known as statistical inference (hypothesis testing).

7. Forecasting or Prediction To illustrate, suppose we want to predict the mean consumption expenditure for 1997. The GDP value for 1997 was 7269.8 billion dollars consumption would be: Yˆ1997 = −184.0779 + 0.7064 (7269.8) = 4951.3 (I.3.4) The actual value of the consumption expenditure reported in 1997 was 4913.5 billion dollars. The estimated model (I.3.3) thus over-predicted the actual consumption expenditure by about 37.82 billion dollars. We could say the forecast error is about 37.8 billion dollars, which is about 0.76 percent of the actual GDP value for 1997. Now suppose the government decides to propose a reduction in the income tax. What will be the effect of such a policy on income and thereby on consumption expenditure and ultimately on employment?

Suppose that, as a result of the proposed policy change, investment expenditure increases. What will be the effect on the economy? As macroeconomic theory shows, the change in income following, a dollar’s worth of change in investment expenditure is given by the income multiplier M, which is defined as: M = 1/(1 − MPC) (I.3.5) The multiplier is about M = 3.33. That is, an increase (decrease) of a dollar in investment will eventually lead to more than a threefold increase (decrease) in income; note that it takes time for the multiplier to work. The critical value in this computation is MPC. Thus, a quantitative estimate of MPC provides valuable information for policy purposes. Knowing MPC, one can predict the future course of income, consumption expenditure, and employment following a change in the government’s fiscal policies.

8. Use of the Model for Control or Policy Purposes Suppose we have the estimated consumption function given in (I.3.3). Suppose further the government believes that consumer expenditure of about 4900 will keep the unemployment rate at its current level of about 4.2%. What level of income will guarantee the target amount of consumption expenditure? If the regression results given in (I.3.3) seem reasonable, simple arithmetic will show that: 4900 = −184.0779 + 0.7064X (I.3.6) which gives X = 7197, approximately. That is, an income level of about 7197 (billion) dollars, given an MPC of about 0.70, will produce an expenditure of about 4900 billion dollars. As these calculations suggest, an estimated model may be used for control, or policy, purposes. By appropriate fiscal and monetary policy mix, the government can manipulate the control variable X to produce the desired level of the target variable Y.

Figure I.4 summarizes the anatomy of classical econometric modeling.

Choosing among Competing Models When a governmental agency (e.g., the U.S. Department of Commerce) collects economic data, such as that shown in Table I.1, it does not necessarily have any economic theory in mind. How then does one know that the data really support the Keynesian theory of consumption? Is it because the Keynesian consumption function (i.e., the regression line) shown in Figure I.3 is extremely close to the actual data points? Is it possible that another consumption model (theory) might equally fit the data as well? For example, Milton Friedman has developed a model of consumption, called the permanent income hypothesis. Robert Hall has also developed a model of consumption, called the life-cycle permanent income hypothesis. Could one or both of these models also fit the data in Table I.1? In short, the question facing a researcher in practice is how to choose among competing hypotheses or models of a given phenomenon, such as the consumption–income relationship.

The eight-step classical econometric methodology discussed above is neutral in the sense that it can be used to test any of these rival hypotheses. Is it possible to develop a methodology that is comprehensive enough to include competing hypotheses? This is an involved and controversial topic.